How to make targeted offers to customers?¶
This tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming model, and get its solution by solving the model with IBM ILOG CPLEX Optimizer.
When you finish this tutorial, you’ll have a foundational knowledge of Prescriptive Analytics.
This notebook is part of Prescriptive Analytics for Python
It requires either an installation of CPLEX Optimizers or it can be run on IBM Watson Studio Cloud (Sign up for a free IBM Cloud account and you can start using Watson Studio Cloud right away).
Table of contents:
- Describe the business problem
- How decision optimization can help
- Prepare the data
- Use IBM Decision Optimization CPLEX Modeling for Python
Describe the business problem¶
- The Self-Learning Response Model (SLRM) node enables you to build a model that you can continually update. Such updates are useful in building a model that assists with predicting which offers are most appropriate for customers and the probability of the offers being accepted. These sorts of models are most beneficial in customer relationship management, such as marketing applications or call centers.
- This example is based on a fictional banking company.
- The marketing department wants to achieve more profitable results in future campaigns by matching the right offer of financial services to each customer.
- Specifically, the datascience department identified the characteristics of customers who are most likely to respond favorably based on previous offers and responses and to promote the best current offer based on the results and now need to compute the best offerig plan.
A set of business constraints have to be respected:
- We have a limited budget to run a marketing campaign based on “gifts”, “newsletter”, “seminar”.
- We want to determine which is the best way to contact the customers.
- We need to identify which customers to contact.
How decision optimization can help¶
Prescriptive analytics technology recommends actions based on desired outcomes, taking into account specific scenarios, resources, and knowledge of past and current events. This insight can help your organization make better decisions and have greater control of business outcomes.
Prescriptive analytics is the next step on the path to insight-based actions. It creates value through synergy with predictive analytics, which analyzes data to predict future outcomes.
- Prescriptive analytics takes that insight to the next level by suggesting the optimal way to handle that future situation. Organizations that can act fast in dynamic conditions and make superior decisions in uncertain environments gain a strong competitive advantage.
For example:
- Automate complex decisions and trade-offs to better manage limited resources.
- Take advantage of a future opportunity or mitigate a future risk.
- Proactively update recommendations based on changing events.
- Meet operational goals, increase customer loyalty, prevent threats and fraud, and optimize business processes.
Prepare the data¶
The predictions show which offers a customer is most likely to accept, and the confidence that they will accept, depending on each customer’s details.
For example: (139987, “Pension”, 0.13221, “Mortgage”, 0.10675) indicates that customer Id=139987 will certainly not buy a Pension as the level is only 13.2%, whereas (140030, “Savings”, 0.95678, “Pension”, 0.84446) is more than likely to buy Savings and a Pension as the rates are 95.7% and 84.4%.
This data is taken from a SPSS example, except that the names of the customers were modified.
A Python data analysis library, pandas, is used to store the data. Let’s set up and declare the data.
Offers are stored in a pandas DataFrame.
Let’s customize the display of this data and show the confidence forecast for each customer.
name | Product1 | Confidence1 | Product2 | Confidence2 | |
---|---|---|---|---|---|
17 | Cassio Lombardo | Pension | 0.13221 | Mortgage | 0.10675 |
7 | Christian Austerlitz | Pension | 0.13221 | Mortgage | 0.10675 |
24 | Earl B. Wood | Savings | 0.95678 | Pension | 0.83426 |
19 | Eldar Muravyov | Pension | 0.13221 | Mortgage | 0.10675 |
6 | Fabien Mailhot | Pension | 0.13221 | Mortgage | 0.10675 |
26 | Franca Palermo | Pension | 0.13221 | Mortgage | 0.10675 |
25 | Gabrielly Sousa Martins | Savings | 0.95678 | Pension | 0.75925 |
13 | George Blomqvist | Savings | 0.16428 | Pension | 0.13221 |
0 | Guadalupe J. Martinez | Pension | 0.13221 | Mortgage | 0.10675 |
21 | Jameel Abdul-Ghani Gerges | Pension | 0.13221 | Mortgage | 0.10675 |
10 | Lee Tsou | Pension | 0.13221 | Mortgage | 0.10675 |
23 | Matheus Azevedo Melo | Pension | 0.13221 | Mortgage | 0.10675 |
1 | Michelle M. Lopez | Savings | 0.95678 | Pension | 0.84446 |
3 | Miranda B. Roush | Pension | 0.13221 | Mortgage | 0.10675 |
12 | Miroslav Skaroupka | Savings | 0.95676 | Mortgage | 0.82269 |
5 | Roland Gu�rette | Pension | 0.13221 | Mortgage | 0.10675 |
11 | Sanaa' Hikmah Hakimi | Pension | 0.13221 | Mortgage | 0.10675 |
4 | Sandra J. Wynkoop | Pension | 0.80506 | Savings | 0.28391 |
20 | Shu T'an | Savings | 0.95675 | Pension | 0.27248 |
8 | Steffen Meister | Pension | 0.13221 | Mortgage | 0.10675 |
2 | Terry L. Ridgley | Savings | 0.95678 | Pension | 0.80233 |
18 | Trinity Zelaya Miramontes | Savings | 0.28934 | Pension | 0.13221 |
16 | Vlad Alekseeva | Pension | 0.13221 | Mortgage | 0.10675 |
14 | Will Henderson | Savings | 0.95678 | Pension | 0.86779 |
9 | Wolfgang Sanger | Pension | 0.13221 | Mortgage | 0.10675 |
15 | Yuina Ohira | Pension | 0.13225 | Mortgage | 0.10675 |
22 | Zeeb Longoria Marrero | Savings | 0.16188 | Pension | 0.13221 |
Use IBM Decision Optimization CPLEX Modeling for Python¶
Let’s create the optimization model to select the best ways to contact customers and stay within the limited budget.
Step 1: Import the library¶
Run the following code to import the Decision Optimization CPLEX Modeling library. The DOcplex library contains the two modeling packages, Mathematical Programming (docplex.mp) and Constraint Programming (docplex.cp).
If cplex is not installed, install CPLEX Community edition.
Step 2: Set up the prescriptive model¶
Create the model¶
Define the decision variables¶
- The integer decision variables
channelVars
, represent whether or not a customer will be made an offer for a particular product via a particular channel. - The integer decision variable
totaloffers
represents the total number of offers made. - The continuous variable
budgetSpent
represents the total cost of the offers made.
Set up the constraints¶
- Offer only one product per customer.
- Compute the budget and set a maximum on it.
- Compute the number of offers to be made.
Model: marketing_campaign
- number of variables: 326
- binary=324, integer=1, continuous=1
- number of constraints: 34
- linear=34
- parameters: defaults
- problem type is: MILP
Express the objective¶
We want to maximize the expected revenue.
Solve the model¶
If you’re using a Community Edition of CPLEX runtimes, depending on the size of the problem, the solve stage may fail and will need a paying subscription or product installation.
Step 3: Analyze the solution¶
First, let’s display the Optimal Marketing Channel per customer.
Marketing plan has 20 offers costing 364.0
channel | product | customer | |
---|---|---|---|
0 | newsletter | Car loan | Fabien Mailhot |
1 | newsletter | Car loan | Christian Austerlitz |
2 | newsletter | Car loan | Lee Tsou |
3 | newsletter | Car loan | Sanaa' Hikmah Hakimi |
4 | newsletter | Car loan | George Blomqvist |
5 | newsletter | Car loan | Yuina Ohira |
6 | newsletter | Car loan | Vlad Alekseeva |
7 | newsletter | Car loan | Cassio Lombardo |
8 | newsletter | Car loan | Trinity Zelaya Miramontes |
9 | newsletter | Car loan | Eldar Muravyov |
10 | newsletter | Car loan | Jameel Abdul-Ghani Gerges |
11 | newsletter | Car loan | Zeeb Longoria Marrero |
12 | seminar | Savings | Terry L. Ridgley |
13 | seminar | Savings | Gabrielly Sousa Martins |
14 | seminar | Mortgage | Miranda B. Roush |
15 | seminar | Mortgage | Miroslav Skaroupka |
16 | seminar | Mortgage | Matheus Azevedo Melo |
17 | seminar | Mortgage | Franca Palermo |
18 | seminar | Pension | Michelle M. Lopez |
19 | seminar | Pension | Will Henderson |
Then let’s focus on seminar.
product | customer | |
---|---|---|
12 | Savings | Terry L. Ridgley |
13 | Savings | Gabrielly Sousa Martins |
14 | Mortgage | Miranda B. Roush |
15 | Mortgage | Miroslav Skaroupka |
16 | Mortgage | Matheus Azevedo Melo |
17 | Mortgage | Franca Palermo |
18 | Pension | Michelle M. Lopez |
19 | Pension | Will Henderson |
Summary¶
You learned how to set up and use IBM Decision Optimization CPLEX Modeling for Python to formulate a Mathematical Programming model and solve it with CPLEX.
References¶
- CPLEX Modeling for Python documentation
- Decision Optimization on Cloud
- Need help with DOcplex or to report a bug? Please go here.
- Contact us at dofeedback@wwpdl.vnet.ibm.com.
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